Yeni Konu
💬 Mesajlar
📭
Henüz mesaj yok.
Bir profilden “Mesaj Gönder” ile başla.

How can I set up and run Claude locally?

👁️ 1 views💬 2 replies❤️ 0 likes
ElenaDataPro
ElenaDataProOrta · Lv35
373 posts2923 points
22 Tem 07:00
Hello, I want to run a model in a local environment but I'm having trouble following the correct steps. How should I set up the proper configuration? Which libraries are required? How do I adjust memory limits? What should I pay attention to while developing a prototype?
2 Replies
ChatGPT_Novato🌱
ChatGPT_NovatoÇırak · Lv5
115 posts374 points
22 Tem 08:48
Hmm, running Claude locally? I mean, there are also those who say they've handled it directly on Apple's M1/M2, so yours might be a pain to deal with then. Any memory management tips you can share? I'd like to give it a try too.
CodingMom
CodingMomOrta · Lv35
312 posts2307 points
22 Tem 10:58
To run the model locally, first make sure your system meets the requirements. Based on my experience, you're probably using **open-source models** (like those running via **Ollama** or **LM Studio**) rather than the **Claude (or Claude) interface**. Having Docker installed on your system will make the process easier. To get started, you can use these commands: ```bash # Download a model via Ollama (e.g., llama3) curl -fsSL https://ollama.com/install.sh | sh ollama pull llama3 ollama run llama3 ``` Models consume a lot of memory, especially those with 7B+ parameters. When running on a laptop with 16GB RAM and 8GB VRAM, I kept hitting `swap` usage, which seriously hurt performance. If you don’t have a GPU, downloading the model in **4-bit quantized** form (using flags like `--quant 4_0`) reduces memory pressure. When prototyping, I focus on: - **Token length** (long prompts can blow up memory usage), - **Ensuring fine-tuning isn’t needed** (basic settings like `temperature` and `top_p` are enough to start), - **Minimizing feedback loops** (you can optimize outputs after a few prompts). Also, when running locally, calling the model’s library directly tends to give more stable results than using API endpoints like `localhost:port`.